use anyhow::{Result, anyhow};
use collections::{BTreeMap, HashMap};
use futures::Stream;
use futures::{FutureExt, StreamExt, future, future::BoxFuture};
use gpui::{AnyView, App, AsyncApp, Context, Entity, SharedString, Task, Window};
use http_client::HttpClient;
use language_model::{
    AuthenticateError, LanguageModel, LanguageModelCompletionError, LanguageModelCompletionEvent,
    LanguageModelId, LanguageModelName, LanguageModelProvider, LanguageModelProviderId,
    LanguageModelProviderName, LanguageModelProviderState, LanguageModelRequest,
    LanguageModelToolChoice, LanguageModelToolResultContent, LanguageModelToolUse, MessageContent,
    RateLimiter, Role, StopReason, TokenUsage,
};
use menu;
use open_ai::{
    ImageUrl, Model, OPEN_AI_API_URL, ReasoningEffort, ResponseStreamEvent, stream_completion,
};
use settings::{OpenAiAvailableModel as AvailableModel, Settings, SettingsStore};
use std::pin::Pin;
use std::str::FromStr as _;
use std::sync::{Arc, LazyLock};
use strum::IntoEnumIterator;
use ui::{List, prelude::*};
use ui_input::InputField;
use util::ResultExt;
use zed_env_vars::{EnvVar, env_var};

use crate::ui::ConfiguredApiCard;
use crate::{api_key::ApiKeyState, ui::InstructionListItem};

const PROVIDER_ID: LanguageModelProviderId = language_model::OPEN_AI_PROVIDER_ID;
const PROVIDER_NAME: LanguageModelProviderName = language_model::OPEN_AI_PROVIDER_NAME;

const API_KEY_ENV_VAR_NAME: &str = "OPENAI_API_KEY";
static API_KEY_ENV_VAR: LazyLock<EnvVar> = env_var!(API_KEY_ENV_VAR_NAME);

#[derive(Default, Clone, Debug, PartialEq)]
pub struct OpenAiSettings {
    pub api_url: String,
    pub available_models: Vec<AvailableModel>,
}

pub struct OpenAiLanguageModelProvider {
    http_client: Arc<dyn HttpClient>,
    state: Entity<State>,
}

pub struct State {
    api_key_state: ApiKeyState,
}

impl State {
    fn is_authenticated(&self) -> bool {
        self.api_key_state.has_key()
    }

    fn set_api_key(&mut self, api_key: Option<String>, cx: &mut Context<Self>) -> Task<Result<()>> {
        let api_url = OpenAiLanguageModelProvider::api_url(cx);
        self.api_key_state
            .store(api_url, api_key, |this| &mut this.api_key_state, cx)
    }

    fn authenticate(&mut self, cx: &mut Context<Self>) -> Task<Result<(), AuthenticateError>> {
        let api_url = OpenAiLanguageModelProvider::api_url(cx);
        self.api_key_state.load_if_needed(
            api_url,
            &API_KEY_ENV_VAR,
            |this| &mut this.api_key_state,
            cx,
        )
    }
}

impl OpenAiLanguageModelProvider {
    pub fn new(http_client: Arc<dyn HttpClient>, cx: &mut App) -> Self {
        let state = cx.new(|cx| {
            cx.observe_global::<SettingsStore>(|this: &mut State, cx| {
                let api_url = Self::api_url(cx);
                this.api_key_state.handle_url_change(
                    api_url,
                    &API_KEY_ENV_VAR,
                    |this| &mut this.api_key_state,
                    cx,
                );
                cx.notify();
            })
            .detach();
            State {
                api_key_state: ApiKeyState::new(Self::api_url(cx)),
            }
        });

        Self { http_client, state }
    }

    fn create_language_model(&self, model: open_ai::Model) -> Arc<dyn LanguageModel> {
        Arc::new(OpenAiLanguageModel {
            id: LanguageModelId::from(model.id().to_string()),
            model,
            state: self.state.clone(),
            http_client: self.http_client.clone(),
            request_limiter: RateLimiter::new(4),
        })
    }

    fn settings(cx: &App) -> &OpenAiSettings {
        &crate::AllLanguageModelSettings::get_global(cx).openai
    }

    fn api_url(cx: &App) -> SharedString {
        let api_url = &Self::settings(cx).api_url;
        if api_url.is_empty() {
            open_ai::OPEN_AI_API_URL.into()
        } else {
            SharedString::new(api_url.as_str())
        }
    }
}

impl LanguageModelProviderState for OpenAiLanguageModelProvider {
    type ObservableEntity = State;

    fn observable_entity(&self) -> Option<Entity<Self::ObservableEntity>> {
        Some(self.state.clone())
    }
}

impl LanguageModelProvider for OpenAiLanguageModelProvider {
    fn id(&self) -> LanguageModelProviderId {
        PROVIDER_ID
    }

    fn name(&self) -> LanguageModelProviderName {
        PROVIDER_NAME
    }

    fn icon(&self) -> IconName {
        IconName::AiOpenAi
    }

    fn default_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
        Some(self.create_language_model(open_ai::Model::default()))
    }

    fn default_fast_model(&self, _cx: &App) -> Option<Arc<dyn LanguageModel>> {
        Some(self.create_language_model(open_ai::Model::default_fast()))
    }

    fn provided_models(&self, cx: &App) -> Vec<Arc<dyn LanguageModel>> {
        let mut models = BTreeMap::default();

        // Add base models from open_ai::Model::iter()
        for model in open_ai::Model::iter() {
            if !matches!(model, open_ai::Model::Custom { .. }) {
                models.insert(model.id().to_string(), model);
            }
        }

        // Override with available models from settings
        for model in &OpenAiLanguageModelProvider::settings(cx).available_models {
            models.insert(
                model.name.clone(),
                open_ai::Model::Custom {
                    name: model.name.clone(),
                    display_name: model.display_name.clone(),
                    max_tokens: model.max_tokens,
                    max_output_tokens: model.max_output_tokens,
                    max_completion_tokens: model.max_completion_tokens,
                    reasoning_effort: model.reasoning_effort.clone(),
                },
            );
        }

        models
            .into_values()
            .map(|model| self.create_language_model(model))
            .collect()
    }

    fn is_authenticated(&self, cx: &App) -> bool {
        self.state.read(cx).is_authenticated()
    }

    fn authenticate(&self, cx: &mut App) -> Task<Result<(), AuthenticateError>> {
        self.state.update(cx, |state, cx| state.authenticate(cx))
    }

    fn configuration_view(
        &self,
        _target_agent: language_model::ConfigurationViewTargetAgent,
        window: &mut Window,
        cx: &mut App,
    ) -> AnyView {
        cx.new(|cx| ConfigurationView::new(self.state.clone(), window, cx))
            .into()
    }

    fn reset_credentials(&self, cx: &mut App) -> Task<Result<()>> {
        self.state
            .update(cx, |state, cx| state.set_api_key(None, cx))
    }
}

pub struct OpenAiLanguageModel {
    id: LanguageModelId,
    model: open_ai::Model,
    state: Entity<State>,
    http_client: Arc<dyn HttpClient>,
    request_limiter: RateLimiter,
}

impl OpenAiLanguageModel {
    fn stream_completion(
        &self,
        request: open_ai::Request,
        cx: &AsyncApp,
    ) -> BoxFuture<'static, Result<futures::stream::BoxStream<'static, Result<ResponseStreamEvent>>>>
    {
        let http_client = self.http_client.clone();

        let Ok((api_key, api_url)) = self.state.read_with(cx, |state, cx| {
            let api_url = OpenAiLanguageModelProvider::api_url(cx);
            (state.api_key_state.key(&api_url), api_url)
        }) else {
            return future::ready(Err(anyhow!("App state dropped"))).boxed();
        };

        let future = self.request_limiter.stream(async move {
            let provider = PROVIDER_NAME;
            let Some(api_key) = api_key else {
                return Err(LanguageModelCompletionError::NoApiKey { provider });
            };
            let request = stream_completion(
                http_client.as_ref(),
                provider.0.as_str(),
                &api_url,
                &api_key,
                request,
            );
            let response = request.await?;
            Ok(response)
        });

        async move { Ok(future.await?.boxed()) }.boxed()
    }
}

impl LanguageModel for OpenAiLanguageModel {
    fn id(&self) -> LanguageModelId {
        self.id.clone()
    }

    fn name(&self) -> LanguageModelName {
        LanguageModelName::from(self.model.display_name().to_string())
    }

    fn provider_id(&self) -> LanguageModelProviderId {
        PROVIDER_ID
    }

    fn provider_name(&self) -> LanguageModelProviderName {
        PROVIDER_NAME
    }

    fn supports_tools(&self) -> bool {
        true
    }

    fn supports_images(&self) -> bool {
        use open_ai::Model;
        match &self.model {
            Model::FourOmni
            | Model::FourOmniMini
            | Model::FourPointOne
            | Model::FourPointOneMini
            | Model::FourPointOneNano
            | Model::Five
            | Model::FiveMini
            | Model::FiveNano
            | Model::O1
            | Model::O3
            | Model::O4Mini => true,
            Model::ThreePointFiveTurbo
            | Model::Four
            | Model::FourTurbo
            | Model::O3Mini
            | Model::Custom { .. } => false,
        }
    }

    fn supports_tool_choice(&self, choice: LanguageModelToolChoice) -> bool {
        match choice {
            LanguageModelToolChoice::Auto => true,
            LanguageModelToolChoice::Any => true,
            LanguageModelToolChoice::None => true,
        }
    }

    fn telemetry_id(&self) -> String {
        format!("openai/{}", self.model.id())
    }

    fn max_token_count(&self) -> u64 {
        self.model.max_token_count()
    }

    fn max_output_tokens(&self) -> Option<u64> {
        self.model.max_output_tokens()
    }

    fn count_tokens(
        &self,
        request: LanguageModelRequest,
        cx: &App,
    ) -> BoxFuture<'static, Result<u64>> {
        count_open_ai_tokens(request, self.model.clone(), cx)
    }

    fn stream_completion(
        &self,
        request: LanguageModelRequest,
        cx: &AsyncApp,
    ) -> BoxFuture<
        'static,
        Result<
            futures::stream::BoxStream<
                'static,
                Result<LanguageModelCompletionEvent, LanguageModelCompletionError>,
            >,
            LanguageModelCompletionError,
        >,
    > {
        let request = into_open_ai(
            request,
            self.model.id(),
            self.model.supports_parallel_tool_calls(),
            self.model.supports_prompt_cache_key(),
            self.max_output_tokens(),
            self.model.reasoning_effort(),
        );
        let completions = self.stream_completion(request, cx);
        async move {
            let mapper = OpenAiEventMapper::new();
            Ok(mapper.map_stream(completions.await?).boxed())
        }
        .boxed()
    }
}

pub fn into_open_ai(
    request: LanguageModelRequest,
    model_id: &str,
    supports_parallel_tool_calls: bool,
    supports_prompt_cache_key: bool,
    max_output_tokens: Option<u64>,
    reasoning_effort: Option<ReasoningEffort>,
) -> open_ai::Request {
    let stream = !model_id.starts_with("o1-");

    let mut messages = Vec::new();
    for message in request.messages {
        for content in message.content {
            match content {
                MessageContent::Text(text) | MessageContent::Thinking { text, .. } => {
                    if !text.trim().is_empty() {
                        add_message_content_part(
                            open_ai::MessagePart::Text { text },
                            message.role,
                            &mut messages,
                        );
                    }
                }
                MessageContent::RedactedThinking(_) => {}
                MessageContent::Image(image) => {
                    add_message_content_part(
                        open_ai::MessagePart::Image {
                            image_url: ImageUrl {
                                url: image.to_base64_url(),
                                detail: None,
                            },
                        },
                        message.role,
                        &mut messages,
                    );
                }
                MessageContent::ToolUse(tool_use) => {
                    let tool_call = open_ai::ToolCall {
                        id: tool_use.id.to_string(),
                        content: open_ai::ToolCallContent::Function {
                            function: open_ai::FunctionContent {
                                name: tool_use.name.to_string(),
                                arguments: serde_json::to_string(&tool_use.input)
                                    .unwrap_or_default(),
                            },
                        },
                    };

                    if let Some(open_ai::RequestMessage::Assistant { tool_calls, .. }) =
                        messages.last_mut()
                    {
                        tool_calls.push(tool_call);
                    } else {
                        messages.push(open_ai::RequestMessage::Assistant {
                            content: None,
                            tool_calls: vec![tool_call],
                        });
                    }
                }
                MessageContent::ToolResult(tool_result) => {
                    let content = match &tool_result.content {
                        LanguageModelToolResultContent::Text(text) => {
                            vec![open_ai::MessagePart::Text {
                                text: text.to_string(),
                            }]
                        }
                        LanguageModelToolResultContent::Image(image) => {
                            vec![open_ai::MessagePart::Image {
                                image_url: ImageUrl {
                                    url: image.to_base64_url(),
                                    detail: None,
                                },
                            }]
                        }
                    };

                    messages.push(open_ai::RequestMessage::Tool {
                        content: content.into(),
                        tool_call_id: tool_result.tool_use_id.to_string(),
                    });
                }
            }
        }
    }

    open_ai::Request {
        model: model_id.into(),
        messages,
        stream,
        stop: request.stop,
        temperature: request.temperature.unwrap_or(1.0),
        max_completion_tokens: max_output_tokens,
        parallel_tool_calls: if supports_parallel_tool_calls && !request.tools.is_empty() {
            // Disable parallel tool calls, as the Agent currently expects a maximum of one per turn.
            Some(false)
        } else {
            None
        },
        prompt_cache_key: if supports_prompt_cache_key {
            request.thread_id
        } else {
            None
        },
        tools: request
            .tools
            .into_iter()
            .map(|tool| open_ai::ToolDefinition::Function {
                function: open_ai::FunctionDefinition {
                    name: tool.name,
                    description: Some(tool.description),
                    parameters: Some(tool.input_schema),
                },
            })
            .collect(),
        tool_choice: request.tool_choice.map(|choice| match choice {
            LanguageModelToolChoice::Auto => open_ai::ToolChoice::Auto,
            LanguageModelToolChoice::Any => open_ai::ToolChoice::Required,
            LanguageModelToolChoice::None => open_ai::ToolChoice::None,
        }),
        reasoning_effort,
    }
}

fn add_message_content_part(
    new_part: open_ai::MessagePart,
    role: Role,
    messages: &mut Vec<open_ai::RequestMessage>,
) {
    match (role, messages.last_mut()) {
        (Role::User, Some(open_ai::RequestMessage::User { content }))
        | (
            Role::Assistant,
            Some(open_ai::RequestMessage::Assistant {
                content: Some(content),
                ..
            }),
        )
        | (Role::System, Some(open_ai::RequestMessage::System { content, .. })) => {
            content.push_part(new_part);
        }
        _ => {
            messages.push(match role {
                Role::User => open_ai::RequestMessage::User {
                    content: open_ai::MessageContent::from(vec![new_part]),
                },
                Role::Assistant => open_ai::RequestMessage::Assistant {
                    content: Some(open_ai::MessageContent::from(vec![new_part])),
                    tool_calls: Vec::new(),
                },
                Role::System => open_ai::RequestMessage::System {
                    content: open_ai::MessageContent::from(vec![new_part]),
                },
            });
        }
    }
}

pub struct OpenAiEventMapper {
    tool_calls_by_index: HashMap<usize, RawToolCall>,
}

impl OpenAiEventMapper {
    pub fn new() -> Self {
        Self {
            tool_calls_by_index: HashMap::default(),
        }
    }

    pub fn map_stream(
        mut self,
        events: Pin<Box<dyn Send + Stream<Item = Result<ResponseStreamEvent>>>>,
    ) -> impl Stream<Item = Result<LanguageModelCompletionEvent, LanguageModelCompletionError>>
    {
        events.flat_map(move |event| {
            futures::stream::iter(match event {
                Ok(event) => self.map_event(event),
                Err(error) => vec![Err(LanguageModelCompletionError::from(anyhow!(error)))],
            })
        })
    }

    pub fn map_event(
        &mut self,
        event: ResponseStreamEvent,
    ) -> Vec<Result<LanguageModelCompletionEvent, LanguageModelCompletionError>> {
        let mut events = Vec::new();
        if let Some(usage) = event.usage {
            events.push(Ok(LanguageModelCompletionEvent::UsageUpdate(TokenUsage {
                input_tokens: usage.prompt_tokens,
                output_tokens: usage.completion_tokens,
                cache_creation_input_tokens: 0,
                cache_read_input_tokens: 0,
            })));
        }

        let Some(choice) = event.choices.first() else {
            return events;
        };

        if let Some(delta) = choice.delta.as_ref() {
            if let Some(content) = delta.content.clone() {
                events.push(Ok(LanguageModelCompletionEvent::Text(content)));
            }

            if let Some(tool_calls) = delta.tool_calls.as_ref() {
                for tool_call in tool_calls {
                    let entry = self.tool_calls_by_index.entry(tool_call.index).or_default();

                    if let Some(tool_id) = tool_call.id.clone() {
                        entry.id = tool_id;
                    }

                    if let Some(function) = tool_call.function.as_ref() {
                        if let Some(name) = function.name.clone() {
                            entry.name = name;
                        }

                        if let Some(arguments) = function.arguments.clone() {
                            entry.arguments.push_str(&arguments);
                        }
                    }
                }
            }
        }

        match choice.finish_reason.as_deref() {
            Some("stop") => {
                events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
            }
            Some("tool_calls") => {
                events.extend(self.tool_calls_by_index.drain().map(|(_, tool_call)| {
                    match serde_json::Value::from_str(&tool_call.arguments) {
                        Ok(input) => Ok(LanguageModelCompletionEvent::ToolUse(
                            LanguageModelToolUse {
                                id: tool_call.id.clone().into(),
                                name: tool_call.name.as_str().into(),
                                is_input_complete: true,
                                input,
                                raw_input: tool_call.arguments.clone(),
                                thought_signature: None,
                            },
                        )),
                        Err(error) => Ok(LanguageModelCompletionEvent::ToolUseJsonParseError {
                            id: tool_call.id.into(),
                            tool_name: tool_call.name.into(),
                            raw_input: tool_call.arguments.clone().into(),
                            json_parse_error: error.to_string(),
                        }),
                    }
                }));

                events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::ToolUse)));
            }
            Some(stop_reason) => {
                log::error!("Unexpected OpenAI stop_reason: {stop_reason:?}",);
                events.push(Ok(LanguageModelCompletionEvent::Stop(StopReason::EndTurn)));
            }
            None => {}
        }

        events
    }
}

#[derive(Default)]
struct RawToolCall {
    id: String,
    name: String,
    arguments: String,
}

pub(crate) fn collect_tiktoken_messages(
    request: LanguageModelRequest,
) -> Vec<tiktoken_rs::ChatCompletionRequestMessage> {
    request
        .messages
        .into_iter()
        .map(|message| tiktoken_rs::ChatCompletionRequestMessage {
            role: match message.role {
                Role::User => "user".into(),
                Role::Assistant => "assistant".into(),
                Role::System => "system".into(),
            },
            content: Some(message.string_contents()),
            name: None,
            function_call: None,
        })
        .collect::<Vec<_>>()
}

pub fn count_open_ai_tokens(
    request: LanguageModelRequest,
    model: Model,
    cx: &App,
) -> BoxFuture<'static, Result<u64>> {
    cx.background_spawn(async move {
        let messages = collect_tiktoken_messages(request);

        match model {
            Model::Custom { max_tokens, .. } => {
                let model = if max_tokens >= 100_000 {
                    // If the max tokens is 100k or more, it is likely the o200k_base tokenizer from gpt4o
                    "gpt-4o"
                } else {
                    // Otherwise fallback to gpt-4, since only cl100k_base and o200k_base are
                    // supported with this tiktoken method
                    "gpt-4"
                };
                tiktoken_rs::num_tokens_from_messages(model, &messages)
            }
            // Currently supported by tiktoken_rs
            // Sometimes tiktoken-rs is behind on model support. If that is the case, make a new branch
            // arm with an override. We enumerate all supported models here so that we can check if new
            // models are supported yet or not.
            Model::ThreePointFiveTurbo
            | Model::Four
            | Model::FourTurbo
            | Model::FourOmni
            | Model::FourOmniMini
            | Model::FourPointOne
            | Model::FourPointOneMini
            | Model::FourPointOneNano
            | Model::O1
            | Model::O3
            | Model::O3Mini
            | Model::O4Mini => tiktoken_rs::num_tokens_from_messages(model.id(), &messages),
            // GPT-5 models don't have tiktoken support yet; fall back on gpt-4o tokenizer
            Model::Five | Model::FiveMini | Model::FiveNano => {
                tiktoken_rs::num_tokens_from_messages("gpt-4o", &messages)
            }
        }
        .map(|tokens| tokens as u64)
    })
    .boxed()
}

struct ConfigurationView {
    api_key_editor: Entity<InputField>,
    state: Entity<State>,
    load_credentials_task: Option<Task<()>>,
}

impl ConfigurationView {
    fn new(state: Entity<State>, window: &mut Window, cx: &mut Context<Self>) -> Self {
        let api_key_editor = cx.new(|cx| {
            InputField::new(
                window,
                cx,
                "sk-000000000000000000000000000000000000000000000000",
            )
        });

        cx.observe(&state, |_, _, cx| {
            cx.notify();
        })
        .detach();

        let load_credentials_task = Some(cx.spawn_in(window, {
            let state = state.clone();
            async move |this, cx| {
                if let Some(task) = state
                    .update(cx, |state, cx| state.authenticate(cx))
                    .log_err()
                {
                    // We don't log an error, because "not signed in" is also an error.
                    let _ = task.await;
                }
                this.update(cx, |this, cx| {
                    this.load_credentials_task = None;
                    cx.notify();
                })
                .log_err();
            }
        }));

        Self {
            api_key_editor,
            state,
            load_credentials_task,
        }
    }

    fn save_api_key(&mut self, _: &menu::Confirm, window: &mut Window, cx: &mut Context<Self>) {
        let api_key = self.api_key_editor.read(cx).text(cx).trim().to_string();
        if api_key.is_empty() {
            return;
        }

        // url changes can cause the editor to be displayed again
        self.api_key_editor
            .update(cx, |editor, cx| editor.set_text("", window, cx));

        let state = self.state.clone();
        cx.spawn_in(window, async move |_, cx| {
            state
                .update(cx, |state, cx| state.set_api_key(Some(api_key), cx))?
                .await
        })
        .detach_and_log_err(cx);
    }

    fn reset_api_key(&mut self, window: &mut Window, cx: &mut Context<Self>) {
        self.api_key_editor
            .update(cx, |input, cx| input.set_text("", window, cx));

        let state = self.state.clone();
        cx.spawn_in(window, async move |_, cx| {
            state
                .update(cx, |state, cx| state.set_api_key(None, cx))?
                .await
        })
        .detach_and_log_err(cx);
    }

    fn should_render_editor(&self, cx: &mut Context<Self>) -> bool {
        !self.state.read(cx).is_authenticated()
    }
}

impl Render for ConfigurationView {
    fn render(&mut self, _: &mut Window, cx: &mut Context<Self>) -> impl IntoElement {
        let env_var_set = self.state.read(cx).api_key_state.is_from_env_var();
        let configured_card_label = if env_var_set {
            format!("API key set in {API_KEY_ENV_VAR_NAME} environment variable")
        } else {
            let api_url = OpenAiLanguageModelProvider::api_url(cx);
            if api_url == OPEN_AI_API_URL {
                "API key configured".to_string()
            } else {
                format!("API key configured for {}", api_url)
            }
        };

        let api_key_section = if self.should_render_editor(cx) {
            v_flex()
                .on_action(cx.listener(Self::save_api_key))
                .child(Label::new("To use Zed's agent with OpenAI, you need to add an API key. Follow these steps:"))
                .child(
                    List::new()
                        .child(InstructionListItem::new(
                            "Create one by visiting",
                            Some("OpenAI's console"),
                            Some("https://platform.openai.com/api-keys"),
                        ))
                        .child(InstructionListItem::text_only(
                            "Ensure your OpenAI account has credits",
                        ))
                        .child(InstructionListItem::text_only(
                            "Paste your API key below and hit enter to start using the assistant",
                        )),
                )
                .child(self.api_key_editor.clone())
                .child(
                    Label::new(format!(
                        "You can also assign the {API_KEY_ENV_VAR_NAME} environment variable and restart Zed."
                    ))
                    .size(LabelSize::Small)
                    .color(Color::Muted),
                )
                .child(
                    Label::new(
                        "Note that having a subscription for another service like GitHub Copilot won't work.",
                    )
                    .size(LabelSize::Small).color(Color::Muted),
                )
                .into_any_element()
        } else {
            ConfiguredApiCard::new(configured_card_label)
                .disabled(env_var_set)
                .on_click(cx.listener(|this, _, window, cx| this.reset_api_key(window, cx)))
                .when(env_var_set, |this| {
                    this.tooltip_label(format!("To reset your API key, unset the {API_KEY_ENV_VAR_NAME} environment variable."))
                })
                .into_any_element()
        };

        let compatible_api_section = h_flex()
            .mt_1p5()
            .gap_0p5()
            .flex_wrap()
            .when(self.should_render_editor(cx), |this| {
                this.pt_1p5()
                    .border_t_1()
                    .border_color(cx.theme().colors().border_variant)
            })
            .child(
                h_flex()
                    .gap_2()
                    .child(
                        Icon::new(IconName::Info)
                            .size(IconSize::XSmall)
                            .color(Color::Muted),
                    )
                    .child(Label::new("Zed also supports OpenAI-compatible models.")),
            )
            .child(
                Button::new("docs", "Learn More")
                    .icon(IconName::ArrowUpRight)
                    .icon_size(IconSize::Small)
                    .icon_color(Color::Muted)
                    .on_click(move |_, _window, cx| {
                        cx.open_url("https://zed.dev/docs/ai/llm-providers#openai-api-compatible")
                    }),
            );

        if self.load_credentials_task.is_some() {
            div().child(Label::new("Loading credentials…")).into_any()
        } else {
            v_flex()
                .size_full()
                .child(api_key_section)
                .child(compatible_api_section)
                .into_any()
        }
    }
}

#[cfg(test)]
mod tests {
    use gpui::TestAppContext;
    use language_model::LanguageModelRequestMessage;

    use super::*;

    #[gpui::test]
    fn tiktoken_rs_support(cx: &TestAppContext) {
        let request = LanguageModelRequest {
            thread_id: None,
            prompt_id: None,
            intent: None,
            mode: None,
            messages: vec![LanguageModelRequestMessage {
                role: Role::User,
                content: vec![MessageContent::Text("message".into())],
                cache: false,
            }],
            tools: vec![],
            tool_choice: None,
            stop: vec![],
            temperature: None,
            thinking_allowed: true,
        };

        // Validate that all models are supported by tiktoken-rs
        for model in Model::iter() {
            let count = cx
                .executor()
                .block(count_open_ai_tokens(
                    request.clone(),
                    model,
                    &cx.app.borrow(),
                ))
                .unwrap();
            assert!(count > 0);
        }
    }
}
